identify-fake-email 1.0.5

FastAPI

identify-fake-email

A Python SDK for the Email Validator API — ML-powered email validation that goes beyond blacklists.

Most validators rely on MX records, SMTP checks, or static blacklists. This API combines traditional checks with an XGBoost model that scores the risk of accepting an email based on patterns in the username and domain — catching auto-generated disposable emails that traditional methods miss.

Installation

pip install identify-fake-email

Requirements

• Python 3.7+
• A RapidAPI key (free tier available — 100 requests/month)

Quick Start

from identify_fake_email.client import EmailValidator

client = EmailValidator("YOUR_RAPIDAPI_KEY")

result = client.validate("[e-mail]")
print(f"Valid: {result.valid_email}")
print(f"Name risk: {result.name_risk}")

if result.name_risk > 0.7 or not result.valid_email:
    print("Suspicious - review")
else:
    print("Safe to accept")

Bulk Validation

Validate up to 30 emails per request.

results = client.validate_bulk(["[e-mail]", "[e-mail]"])

for result in results:
    print(f"{result.email}: name_risk={result.name_risk}")

Low-Level Usage

If you need direct access to the underlying SDK:

import identify_fake_email
from identify_fake_email.api.default_api import DefaultApi
from identify_fake_email.configuration import Configuration

RAPIDAPI_KEY = "YOUR_RAPIDAPI_KEY"
RAPIDAPI_HOST = "email-validator-syntax-mx-disposable-risk-detection.p.rapidapi.com"

config = Configuration()
config.host = f"https://{RAPIDAPI_HOST}"

client = DefaultApi(identify_fake_email.ApiClient(config))

result = client.validate_email_validate_get(
    "[e-mail]",
    _headers={
        "x-rapidapi-key": RAPIDAPI_KEY,
        "x-rapidapi-host": RAPIDAPI_HOST
    }
)

Response Fields

Field	Type	Description
email	string	The email address validated
valid_email_structure	bool	Whether the email has valid syntax
is_role	bool	Whether it's a role address (e.g. admin@, support@)
mx_records	bool	Whether the domain has valid MX records
not_disposable	bool	Whether the domain is on the disposable blacklist
new_domain	bool	Whether the domain was recently created
name_risk	float (0–1)	ML risk score for the username — higher means riskier
domain_risk	float (0–1)	ML risk score for the domain — higher means riskier
valid_email	bool	Overall validity based on traditional checks

Risk Scores

name_risk and domain_risk are ML-generated scores between 0 and 1. The higher the score, the more likely the email is fake or disposable.

name_risk is the more reliable of the two — auto-generated usernames follow distinct patterns (random characters, high digit count, unusual consonant/vowel ratios) that the model picks up on. domain_risk is less reliable as auto-generated domains can look similar to legitimate ones.

We recommend using name_risk as your primary signal and combining it with other fields for the most accurate result. The threshold is up to you — 0.7 is a reasonable starting point but you can adjust it for your use case.

Notes

• SMTP validation is intentionally excluded — disposable emails have real mailboxes so SMTP returns valid, adding latency with no benefit
• Bulk validation supports up to 30 emails per request
• The SDK is auto-generated from the OpenAPI spec

Links

• RapidAPI Listing — subscribe and get your API key
• Interactive Swagger Docs — explore all endpoints and parameters
• OpenAPI Spec — source spec and SDK generation workflow

License

MIT